Second, to facilitate the optimization, we propose to improve the cost volume in pixel- and frame- levels, respectively. In the pixel level, a transformer block is asymmetrically applied to the reference view (but not to source views). This way, by capturing long-range global context via a transformer and pixel-wise local features via CNNs, we build an asymmetric cost volume to store more accurate matching similarity cues. In the frame level, we propose a residual pose net to rectify the camera poses, which are usually obtained via Visual SLAM [1]}, [2]}, [3]} and inevitably contain noise, to more accurately backward warp the reference features to match its counterparts in source views.
